İşletmeler İçin makine öğrenimi hizmet stratejisine genel bakış

Year 2024, Volume: 14 Issue: 4, 1901 - 1923

Hasan Tahsin Aytekin

https://doi.org/10.30783/nevsosbilen.1521972

Abstract

Bu araştırmanın amacı, Hizmet Olarak Makine Öğrenimi (MLaaS) platformlarının kurumsal makine öğrenimi projelerinin tasarım ve geliştirme ortamlarındaki etkinliğini ve kullanılabilirliğini değerlendirmektedir. Bu amaçla dört büyük MLaaS sağlayıcısına odaklanan karşılaştırmalı bir analiz yaklaşımı benimsenmiştir. Odaklanılan MLaaS platform sağlayıcıları Amazon SageMaker, Google AI Platform, Microsoft Azure Machine Learning ve IBM Watson Studio'dur. Araştırmada analiz amacıyla kullanılan veriler, ilgili platform sağlayıcıları tarafından sağlanan kamuya açık bilgilerden elde edilmiştir. Araştırma metodolojisi, toplanan verilerin tematik analizini içermekte ve makine öğrenimi ile ilgili temel özellikleri karşılaştırmaktadır. Çalışmada MLaaS'ın farklı uzmanlık ve kaynak seviyelerine sahip işletmeler için erişilebilir araçlar sağlayarak makine öğrenimi çözümlerinin uygulanmasını basitleştirmedeki rolü vurgulamakta ve MLaaS'ı benimsemenin potansiyel faydalarını ve zorluklarını tartışarak, makine öğrenimi projelerinin geliştirilmesi aşamasında bu platformlardan yararlanmayı düşünen işletmeler için içgörüler ortaya koymaktadır. Sonuç olarak MLaaS platformlarının makine öğrenimi modellerinin dağıtımıyla ilgili karmaşıklığı ve maliyeti önemli ölçüde azalttığı ve REST API'leri aracılığıyla mevcut BT altyapılarına sorunsuz bir şekilde entegre olan özel çözümler sunduğu ortaya konmuştur.

Keywords

Makine Öğrenimi, Hizmet Olarak Makine Öğrenimi, Hizmet Olarak Makine Öğrenimi Platformları, Makine Öğrenimi Hizmetleri

Overview of machine learning service strategy for business

Abstract

Due to the increasing demand for machine learning and the need to analyse data, many companies are looking to reduce the cost of developing their own infrastructure by incorporating high-tech solutions into their processes and are turning to cloud-based solutions. As a result, machine learning as a service (MLaaS) platforms have become important in terms of both cost benefits and technical requirements. In this context, the study adopts a comparative analysis approach and examines the Amazon SageMaker, Google AI Platform, Microsoft Azure Machine Learning and IBM Watson Studio platforms. These platforms were selected based on their user base and the breadth of services they offer. Data was obtained from open data sources provided by the platform vendors. The findings show that each platform has its own advantages, but cost and technical requirements play an important role in organisations' choice of platform. Cloud-based MLaaS solutions were also found to offer cost benefits by enabling small teams to work on large datasets. The results show that MLaaS platforms offer enterprises cost-effective, scalable and flexible solutions. However, issues such as privacy and security play an important role in platform selection. Choosing the right platform and ensuring integration with existing systems is critical to the success of enterprise machine learning projects.

Keywords

Machine Learning, Machine Learning as a Service, Machine Learning as a Service Platforms, Machine Learning Services

References

• Abuhaija, B., Alloubani, A., Almatari, M., Ghaith M. Jaradat, H. B., Abualkishik, A. M., & Alsmadi, M. K. (2023, 04). A comprehensive study of machine learning for predicting cardiovascular disease using Weka and SPSS tools. International Journal of Electrical and Computer Engineering, 13(2), s. 1891-1902. doi:10.11591/ijece.v13i2.pp1891-1902
• Alpaydın, E. (2020). Introduction to Machine Learning, Fourth Edition. Cambridge: MIT Press.
• Amazon AWS Machine Learning. (2024, 04 20). Makine Öğrenimi ve Yapay Zeka. Amazon AWS: https://aws.amazon.com/tr/ai/machine-learning/ adresinden alındı Amazon SageMaker. (2024, 04 20). Makine Öğrenimi - Amazon Web Services. Amazon SageMaker: https://aws.amazon.com/tr/sagemaker/ adresinden alındı
• Aytekin, H. T. (2021, 06 30). Makine Öğreniminin Araştırmacıların Veri Analizi Bağlamında Potansiyel Önemi. Ufuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 10(19), s. 85-106.
• Bhattacharjee, B., Boag, S., Doshi, C., Dube, P., Herta, B., Ishakian, V., . . . Mi, J. (2017, 09 08). IBM Deep Learning Service. IBM Journal of Research and Development, 4-5, s. 10:1 - 10:11. doi:10.1147/JRD.2017.2716578
• Borra, P. (2024, 06). Advancing Data Science and AI with Azure Machine Learning: A Comprehensive Review. International Journal of Research Publication and Reviews, 5(6), s. 1825-1831.
• Borra, P. (2024, 06). The Evolution and Impact of Google Cloud Platform in Machine Learning and AI. International Journal of Advanced Research in Science, Communication and Technology, 4(3), s. 72-77. doi:10.48175/IJARSCT-18908
• Breuel, C. (2020, Ocak 4). ML Ops: Machine Learning as an Engineering Discipline. Kasım 2022 tarihinde Towards Data Science: https://towardsdatascience.com/ml-ops-machine-learning-as-an-engineering-discipline-b86ca4874a3f adresinden alındı
• Chang, B. R., Tsai, H.-F., & Lin, Y.-C. (2023). Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches. Computer Modeling in Engineering & Sciences, 2, s. 783-815. doi:10.32604/cmes.2022.020128
• Chapman, M., & Edwards, M. (2011, 05 31). Service Component Architecture Assembly Model Specification Version 1.1. (M. Beisiegel, A. Karmarkar, S. Patil, & M. Rowley, Dü) 11 11, 2022 tarihinde OASIS: https://docs.oasis-open.org/opencsa/sca-assembly/sca-assembly-spec-v1.1-csprd03.html adresinden alındı
• Costa-Climent, R., Haftor, D. M., & Staniewski, M. W. (2024, 12). Using machine learning to create and capture value in the business models of small and medium-sized enterprises. International Journal of Information Management, 73, s. 102637. doi:10.1016/j.ijinfomgt.2023.102637
• Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022, 10). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24, s. 1709–1734. doi:10.1007/s10796-021-10186-w
• Erl, T. (2006). Service-Oriented Architecture: Concepts, Technology, and Design. PEARSON INDIA.
• Géron, A. (2023). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. Boston: O’Reilly Media.
• Gong, Y., Liu, G., Xue, Y., Li, R., & Meng, L. (2023, 10). A survey on dataset quality in machine learning. Information and Software Technology, 162, s. 107268. doi:10.1016/j.infsof.2023.107268
• Google Cloud Machine Learning. (2024, 04 20). Introduction to Vertex AI. Google Cloud: https://cloud.google.com/ai-platform/docs/technical-overview adresinden alındı
• Grigoriadis, I., Vrochidou, E., Tsiatsiou, I., & Papakostas, G. A. (2023). Machine Learning as a Service (MLaaS) - An Enterprise Perspective. Proceedings of International Conference on Data Science and Applications (Cilt 2, s. 261–273). içinde doi:10.1007/978-981-19-6634-7_19
• IBM - Deep Learning. (2024, 09 25). Deep Learning. Deep learning - Resources and Tools - IBM Developer: https://developer.ibm.com/technologies/deep-learning/ adresinden alındı
• IBM Deep Learning - Articles. (2024, 09 25). Deep Learning - Articles. Deep Learning - Articles - IBM Developer: https://developer.ibm.com/technologies/deep-learning/articles/ adresinden alındı
• IBM Watson Machine Learning. (2024, 04 20). IBM Watson Studio. IBM Watson Machine Learning: https://www.ibm.com/cloud/watson-studio adresinden alındı
• Insani, R., Nasrullah, M., Azizah, A. F., & Raygrandi, E. I. (2023). Application of Data Mining for the Selection Process of Prospective Students at ITTelkom Surabaya by Using the SPSS Modeler. Proceedings of the 3rd International Conference on Advanced Information Scientific Development (ICAISD 2023) (s. 289-294). Jakarta, Indonesia: Science and Technology Publications. doi:10.5220/0012448600003848
• James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Unsupervised Learning. An Introduction to Statistical Learning: with Applications in Python (s. 503–556). içinde Cham: Springer International Publishing. doi:10.1007/978-3-031-38747-0_12
• Kim, M., Stennett, T., Shah, D., Sinha, S., & Orso, A. (2024). Leveraging Large Language Models to Improve REST API Testing. Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results (s. 37–41). Lisbon, Portugal: Association for Computing Machinery. doi:10.1145/3639476.3639769
• Lee, J. H., Shim, H.-J., & Kim, K. K. (2010, 04 14). Critical Success Factors in SOA Implementation: An Exploratory Study. Information Systems Management, 27(2), s. 123-145. doi:10.1080/10580531003685188 Loukides, M. (2012). What is DevOps? Sebastopol, CA 95472: O'Reilly Media, Inc.
• Microsoft, A. M. (2024, 04 20). Microsoft Azure Machine Learning Hizmeti - Hizmet Olarak ML. Microsoft Azure Machine Learning Stduio: https://azure.microsoft.com/products/machine-learning/#product-overview adresinden alındı
• Mira, J., Moreno, I., Bardisbanian, H., & Gorroñogoitia, J. (2024). Chapter 4 Machine Learning (ML) as a Service (MLaas): Enhancing IoT with Intelligence, Adaptive Online Deep and Reinforcement Learning, Model Sharing, and Zero-knowledge Model Verification. R. Sofia, & J. Soldatos içinde, Shaping the Future of IoT with Edge Intelligence: How Edge Computing Enables the Next Generation of IoT Applications (s. Chapter 4). Abingdon (UK): River Publishers. doi:10.1201/9781032632407-6
• Moutaouakal, W. E., & Baïna, K. (2023). Comparative Experimentation of MLOps Power on Microsoft Azure, Amazon Web Services, and Google Cloud Platform. 2023 IEEE 6th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) (s. 1-8). Marrakech, Morocco: IEEE. doi:10.1109/CloudTech58737.2023.10366138
• Nirmala, M., Saravanan, V., Jayasudha, A. R., John, P. M., Privietha, P., & Mahalakshmi, L. (2022, 08). Clinical Implication of Machine Learning Based Cardiovascular Disease Prediction Using IBM Auto AI Service. International Journal for Research in Applied Science & Engineering Technology, 10(8). doi:10.22214/ijraset.2022.46087
• Nykyri, M., Kuisma, M., Hallikas, J., Immonen, M., & Silventoinen, P. (2020). Modeling and Predicting an Industrial Process Using a Neural Network and Automation Data. 2020 IEEE 29th International Symposium on Industrial Electronics (s. 505-509). Delft, Netherlands: IEEE. doi:10.1109/ISIE45063.2020.9152407
• Oliveira, S. d., Topsakal, O., & Toker, O. (2024, 01 21). Benchmarking Automated Machine Learning (AutoML) Frameworks for Object Detection. Information, 15(1), s. 63. doi:10.3390/info15010063
• Pereira, I., Madureira, A., Bettencourt, N., Coelho, D., Rebelo, M. Â., Araújo, C., & Oliveira, D. A. (2024, 04 15). A Machine Learning as a Service (MLaaS) Approach to Improve Marketing Success. Informatics, 11(2), s. 19. doi:10.3390/informatics11020019
• Red Hat. (2024, 04 20). What is a REST API. Red Hat: https://www.redhat.com/en/topics/api/what-is-a-rest-api adresinden alındı
• Ribeiro, M., Grolinger, K., & Capretz, M. A. (2015). MLaaS: Machine Learning as a Service. IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (s. 896-902). Miami: IEEE. doi:10.1109/ICMLA.2015.152
• Sarker, I. H. (2021, 03 22). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), s. 160. doi:10.1007/s42979-021-00592-x
• Shakya, A. K., Pillai, G., & Chakrabarty, S. (2023, 11 30). Reinforcement learning algorithms: A brief survey. Expert Systems with Applications, 231, s. 120495. doi:10.1016/j.eswa.2023.120495
• SPSS, I. (2024, 09 25). IBM SPSS Modeler. IBM SPSS Modeler: https://www.ibm.com/products/spss-modeler adresinden alındı
• Taye, M. M. (2023, 04 25). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12, s. 91. doi:10.3390/computers12050091
• Tufail, S., Riggs, H., Tariq, M., & Sarwat, A. I. (2023, 04 10). Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms. Electronics, 12(8), s. 1789. doi:10.3390/electronics12081789
• Vlisthttps, F. v., Helmondhttps, A., & Ferrarihttps, F. (2024, 03 12). Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence. Big Data & Society, 11(1). doi:10.1177/2053951724123263
• Wendler, T., & Gröttrup, S. (2021). Classification Models. Data Mining with SPSS Modeler. içinde Springer, Cham. doi:10.1007/978-3-030-54338-9_8
• Wendler, T., & Gröttrup, S. (2021). Using R with the Modeler. Data Mining with SPSS Modeler. içinde Springer, Cham. doi:10.1007/978-3-030-54338-9_9
• Xie, S., Xue, Y., Zhu, Y., & Wang, Z. (2022). Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach. IEEE INFOCOM 2022 - IEEE Conference on Computer Communications (s. 2078 - 2087). IEEE Press. doi:10.1109/INFOCOM48880.2022.979670
• Yao, Y., Xiao, Z., Wang, B., Viswanath, B., Zheng, H., & Zhao, B. (2017). Complexity vs. Performance: Empirical Analysis of Machine Learning as a Service. 17th Internet Measurement Conference (IMC) (s. 384-397). London: ACM Inc. New York. doi:10.1145/3131365.3131372
• Zhao, Z., Alzubaidi, L., Zhang, J., Duan, Y., & Gu, Y. (2024, 05 15). A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations. Expert Systems with Applications, 242, s. 122807. doi:10.1016/j.eswa.2023.122807